The Impact of Long-Term Care Home Ownership and Administration Type on All-Cause Mortality from March to April 2020 in Madrid, Spain
Bibliographic record
Abstract
Our aim is to assess whether long-term care home (LTCH) ownership and administration type were associated with all-cause mortality in 470 LTCHs in the Community of Madrid (Spain) during March and April 2020, the first two months of the COVID-19 pandemic. There are eight categories of LTCH type, including various combinations of ownership type (for-profit, nonprofit, and public) and administration type (completely private, private with places rented by the public sector, administrative management by procurement, and completely public). Multilevel regression was used to examine the association between mortality and LTCH type, adjusting for LTCH size, the spread of the COVID-19 infection, and the referral hospital. There were 9468 deaths, a mortality rate of 18.3%. Public and private LTCHs had lower mortality than LTCHs under public-private partnership (PPP) agreements. In the fully adjusted model, mortality was 7.4% (95% CI, 3.1-11.7%) in totally public LTCHs compared with 21.9% (95% CI, 17.4-26.4%) in LTCHs which were publicly owned with administrative management by procurement. These results are a testimony to the fatal consequences that pre-pandemic public-private partnerships in long-term residential care led to during the first months of the COVID-19 pandemic in the Community of Madrid, Spain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".